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Memristor-based neuromorphic computing shows great potential for high speed and high throughput signal processing applications, such as electroencephalogram (EEG) signal processing. Nonetheless, the size of 1T1R memristors arrays is limited by the non-ideality of the devices, which prevents the hardware implementation of large and complex networks. In this work, we propose the Depthwise Separable Convolution and Bidirectional Gate Recurrent Unit (DSC-BiGRU) Network, a lightweight and highly robust hybrid neural network based on 1T1R arrays, which enables efficient processing of EEG signals in the temporal, frequency and spatial domains by hybridizing DSC and BiGRU blocks. The network size is reduced and network robustness is improved while ensuring network classification accuracy. In the simulation, the measured non-idealities of the 1T1R array are brought into the network through statistical analysis. Compared to traditional convolutional networks, the network parameters are reduced by 95% and the network classification accuracy is improved by 21% at 95% array yield rate and 5% tolerable error. This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.

In this study, the sample and multicomponent flux are put into the crucible and fully baked. The color change of multicomponent flux during baking indicates the degree of baking, which is appropriate when multicomponent flux turns light red. Through specific tests, different grades of superalloys are used to carry out the comparison test between full baking and non-baking. The measurement precision after full baking is generally higher than that without baking. The difference in value cannot be predicted. Also, the difference cannot be eliminated by adding a correction factor, it can only be eliminated by full baking. After full baking, it can be seen quantitatively from the blank influence value that the blank has little interference with the measurement of low carbon, and the influence on the measurement of high carbon is negligible.

With the development of aero-engine industry, the main materials of aero-engine, such as superalloys and steels, have higher needs for the detection of trace sulfur. We start from four aspects: ultra-pure composite flux, two-stage calibration at both ends, handling of blank influence and application of Carbon and Sulfur determinator to improve the detection level of trace sulfur. The detection of trace sulfur near 1 ppm has achieved ideal result, and the detection technology has reached the world advanced level.

Due to the superior properties of graphene and the application potential of surface plasmons, the research of graphene surface plasmons has become a hot research direction. Based on the surface plasmons of graphene, this paper has done some researches on the plasma induced transparency, absorption, and slow light effect. The main work and results of this paper are as follows: we have designed a graphene-based metamaterial structure that can realize a dual plasma induced transparency (PIT) effect. The specific structure is formed by the periodic arrangement of graphene bands (as bright mode) and band edge microchips (as bright mode). We use the finite-difference time-domain (FDTD) method to study the dual PIT effect from the aspect of numerical simulation, and then further study the phenomenon of this device from the theoretical fitting of the coupled mode theory (CMT). The CMT model explores the physical mechanism of dual PIT spectral line and obtains a good fitting result. By studying the formation mechanism of the dual PIT effect, we have found that the graphene band as a bright mode interacts with the band edge microchip as a dark mode, and then the dual PIT is formed by destructive interference of the bright and dark modes. In order to better external modulation, the structure only studies the modulation effect caused by the change of Fermi level affected by the external voltage of graphene. Moreover, we also have studied the slow light performance of this structure, and the slow light coefficient reached 0.236 picoseconds (ps). This proposed coupling system of dual PIT effect has important research significance in optical switches, optical loop, and slow light devices.

Memristive stateful logic is one of the most promising candidates to implement an in-memory computing system that computes within the storage unit. It can eliminate the costs for the data movement in the traditional von Neumann system. However, the instability in the memristors is inevitable due to the limitation of the current fabrication technology, which incurs a great challenge for the reliability of the memristive stateful logic. In this paper, the implication of device instability on the reliability of the logic event is simulated. The mathematical relationship between logic reliability and redundancy has been deduced. By combining the mathematical relationship with the vector-matrix multiplication in a memristive crossbar array, the logic error correction scheme with high throughput has been proposed. Moreover, a universal design paradigm has been put forward for complex logic. And the circuit schematic and the flow of the scheme have been raised. Finally, a 1-bit full adder (FA) based on the NOR logic and NOT logic is simulated and the mathematical evaluation is performed. It demonstrates the scheme can improve the reliability of the logic significantly. And compared with other four error corrections, the scheme which can be suitable for all kinds of R–R logics and V–R logics has the best universality and throughput. Compared with the other two approaches which also need additional complementary metal–oxide semiconductor (CMOS) circuits, it needs fewer transistors and cycles for the error correction.

Aiming at the burst vibration fault of a supercritical 600 MW steam turbine generator unit during the on-load operation, the characteristics of sudden vibration faults of the rotor system were compared and analyzed. By analyzing the weight-loss vibration response coefficient and the variation characteristics of the vibration vector, parts falling off fault at the low-low coupling of the rotor system were diagnosed. After inspection, it was confirmed that the burst vibration fault of the rotor system was caused by the falling-off of the blast baffle of the low-low coupling bolt. After the blast baffle was restored by hoisting the installation technique, the vibration of the steam turbine generator unit was stable during start-up and grid connection.

At present, manufacturing models are characterized by multi-variety, small batch, and diversification. It is insufficient to use traditional scheduling methods for production management with high performance. A real-time production scheduling system based on reinforcement learning (RL) is suggested in an effort to address the aforementioned issues. A brand-new manufacturing neural network is created to learn the state-action values for production scheduling in real time using high-dimensional data as the input. The detailed setup of network inputs, neural network, action, and reward are also designed. Then, a policy-based reinforcement learning algorithm is proposed to achieve the optimum objective. Finally, By contrasting the proposed scheduling strategy with rule-based approaches in a smart manufacturing environment, its efficacy is demonstrated. according to experimental data, the suggested algorithm can successfully improve performance in the dynamic job-shop environment.

Selector devices are indispensable components of large-scale memristor array systems. The thereinto, ovonic threshold switching (OTS) selector is one of the most suitable candidates for selector devices, owing to its high selectivity and scalability. However, OTS selectors suffer from poor endurance and stability which are persistent tricky problems for application. Here, we report on a multilayer OTS selector based on simple GeSe and doped-GeSe. The experimental results show improving selector performed extraordinary endurance up to 1010 and the fluctuation of threshold voltage is 2.5%. The reason for the improvement may lie in more interface states which strengthen the interaction among individual layers. These developments pave the way towards tuning a new class of OTS materials engineering, ensuring improvement of electrical performance.

With the development of modern society in China, the application of computer technology is becoming more and more extensive. Today, with the gradual popularization of network technology, related computer network viruses are also constantly appearing. These viruses often pass through the loopholes of the network system and now the computer system, and damage the data and malicious tampering in the system.In order to better protect against the harm of computer network viruses, the related data mining technology has emerged.This paper discusses the type of computer network virus and explores the computer network virus defense system based on data mining technology.

Photocatalytic conversion of carbon dioxide into fuels and valuable chemicals is a promising method for carbon neutralization and solving environmental problems. Through a simple thermal-oxidative exfoliation method, the O element was doped while exfoliated bulk g-C3N4 into ultrathin structure g-C3N4. Benefitting from the ultrathin structure of g-C3N4, the larger surface area and shorter electrons migration distance effectively improve the CO2 reduction efficiency. In addition, density functional thory computation proves that O element doping introduces new impurity energy levels, which making electrons easier to be excited. The prepared photocatalyst reduction of CO2 to CO (116 μmol g−1 h−1) and CH4 (47 μmol g−1 h−1).

A very simple optical tunable device, which can realize multiple functions of frequency selection, reflection and slow light, is presented at the investigation. The proposed device is constructed by a periodic grating-like structure. There are two dielectrics (graphene and silicon) in a period of the equivalent grating. The incident light will strongly resonate with the graphene of electrostatic doping, forming an evanescent wave propagating along the surface of graphene, and this phenomenon is the surface plasmon. Under constructive interference of the polaritons, a unique plasmonic induced transparency phenomenon will be achieved. The induced transparency produced by this device can be well theoretically fitted by the bright and dark mode of optical equivalent cavity which can be called coupled mode theory. This theory can well analyze the influence of various modes and various losses between the function of this device. The device can use gate voltages for electrostatic doping in order to change the graphene carrier concentration and tune the optical performance of the device. Moreover, the length of the device in y-direction is will be much larger than the length of single cycle, providing some basis for realizing the fast tunable function and laying a foundation for the integration. Through a simulation and calculation, we can find that the group index and group delay of this device are as high as 515 and 0.257 picoseconds (ps) respectively, so it can provide a good construction idea for the slow light device. The proposed grating-like metamaterial structure can provide certain simulation and theoretical help for the optical tunable reflectors, absorbers, and slow light devices.

Spin–orbit coupling (SOC) plays a determinate role in spintronics and topological physics. Previous studies indicate that the SOC in graphene nanoribbon (GNR) can be enhanced by the proximity effect from two-dimensional transition-metal dichalcogenide (2D-TMD). However, the bulk inversion symmetry of GNR/2D-TMD restricts further increase of the proximity-induced SOC in GNR. In this view, we introduce a TMD nanoribbon (TMDNR) with finite width, and propose three methods to break the bulk inversion symmetry, i.e. defects in TMDNR, spatial interlayer edge coupling, and twist between GNR and TMDNR, which can further enhance the SOC in the GNR by roughly 30 times, 20 times and 150 times, respectively, depending on the relative energy between the Dirac point of GNR and the states of TMDNR. Furthermore, the significantly enhanced SOC can drive the GNR into a topological superconducting phase. By introducing the Zeeman splitting and s-wave superconductivity in the GNR, quasi one-dimensional topological superconductivity and Majorana zero modes (MZMs) can be achieved in the GNR. At last we propose a feasible experimental method to realize and manipulate MZMs in the GNR/TMDNR system.

Changes in spatial synchrony in the growing season have notable effects on species distribution, cross-trophic ecological interactions and ecosystem stability. These changes, driven by non-uniform climate change were observed on the regional scale. It is still unclear how spatial synchrony of the growing season on the climate gradient of the mid-high latitudes of the Northern Hemisphere and ecoregions, has changed over the past decades. Therefore, we calculated the start, end, and length of the thermal growing season (SOS, EOS, and LOS, respectively), which are indicators of the theoretical plant growth season, based on the daily-mean temperature of the Princeton Global Forcing dataset from 1948 to 2016. Spatial variations in the SOS, EOS and LOS along spatial climate gradients were analyzed using the multivariate-linear regression model. The changes of spatial synchrony in the SOS, EOS and LOS were analyzed using the segmented model. The results showed that in all ecoregions, spatially, areas with higher temperature tended to have an earlier SOS, later EOS and longer LOS. However, not all the areas with higher precipitation tended to have a later SOS, later EOS, and shorter LOS. The spatial synchrony in the SOS decreased across the entire study area, whereas the EOS showed the opposite trend. Among the seven ecoregions, spatial synchrony in the SOS in temperate broadleaf/mixed forests and temperate conifer forests changed the most noticeably, decreasing in both regions. Conversely, spatial synchrony in the EOS in the taiga, temperate grasslands/savannas/shrublands and tundra changed the most noticeably, increasing in each region. These may have important effects on the structure and function of ecosystems, especially on the changes in cross-trophic ecological interactions. Moreover, future climate change may change the spatial synchrony in the SOS and EOS further; however, the actual impact of such ongoing change is largely unknown.

With the rapid development of information technology, research on big data information has gone deep into all walks of life, and massive amounts of data spew out anytime and anywhere. This paper proposes a domain-subordinate relationship acquisition that combines multiple strategies and word representations. From semi-structured text and unstructured text, the candidate entity upper-lower relationship entity pairs are extracted, and then the obtained candidate upper-lower relationship entity pairs are verified by the support vector machine method to obtain high-quality candidate entity pairs. In the training phase, an exploratory method is designed to search for the optimal solution, and a greedy mechanism is introduced to evaluate the effectiveness of the reinforcement learning agent, so that the virtual node mapping scheme can continuously move towards higher system returns. Evolution has finally achieved the optimal virtual network mapping decision, so that large-scale tasks can be deployed to task processing nodes in the appropriate underlying network to achieve efficient task execution in a big data environment. The experimental results show that the Tard method can effectively avoid model overfitting and improve the task recognition processing ability in the actual application process under the premise of meeting large-scale task requests.

The unidirectional reflectionless (UR) light propagation is investigated in the waveguide coupled to gain and loss resonators by using a developed coupled mode-scattering matrix theory. The results show that there is almost no reflection in the case of the backward incidence, but total reflection in the case of the forward incidence under the condition of balancing gain and loss in the gain resonator for the proposed waveguide when the indirect coupling phase θ ranges from 0.8 rad to 2.3 rad and from 4 rad to 5.5 rad. Moreover, the coherent perfect absorption (CPA) can be observed at the same time. Especially, the UR light propagation appears when the absolute value of detuning δ is smaller than 1 × 1013 rad s−1. Based on the findings above, we propose a metal–insulator–metal non-parity-time symmetric plasmonic waveguide and obtain the UR plasmonic propagation and CPA. The theoretical results are in excellent agreement with the finite-difference time-domain simulations. These results will provide a new pathway for the realization of unidirectional propagation and absorption of light at the nanoscale.

Large-scale in situ observations are sorely lacking, leading to poor understanding of nationwide atmospheric turbulence over China. Nevertheless, high-resolution soundings have become available starting in 2011, providing a unique opportunity to investigate turbulence across China. Here, we calculated the mean turbulence dissipation rate (ϵ) from radiosonde measurements across China for the period 2011–2018 using Thorpe analysis. The atmospheric layers that had stronger turbulence indicated by larger ϵ generally came with larger Thorpe length but with smaller Brunt–Väisälä frequency. Overall, the clear-air ϵ in the free atmosphere exhibited large spatial variability with a ‘south-high north-low’ pattern. Large clear-air ϵ values were observed in both the lower stratosphere (LS) and upper troposphere (UT), especially over the Tibetan Plateau (TP) and its neighboring regions with complex terrain likely due to large-amplitude mountain waves. Particularly, less frequent but more intense clear-air turbulence was observed in both lower troposphere (LT) and UT over the TP, while more frequent, less intense clear-air turbulence was found in northern China. The all-sky turbulence considering the moist-saturation effects was much stronger in the troposphere, notably in southern China where convective clouds and precipitation oftentimes dominated. In the vertical direction, the altitude of peak clear-air ϵ in the troposphere was found to decrease poleward, broadly consistent with the meridional gradient of tropopause height in the Northern Hemisphere. A double-peak mode stood out for the profiles of clear-air ϵ at midlatitudes to the north of 30° N in winter: one peak was at altitudes of 15–18 km, and another at altitudes of 5–8 km. The strong shear instabilities around the westerly jet stream could account for the vertical bimodal structures. The seasonality of ϵ was also pronounced, reaching maxima in summer and minima in winter. Our results may help understand and avoid clear-air turbulence, as related to aviation safety among other issues.

This paper studies the related theories of wireless sensor networks, and an improved DV-Hop algorithm is proposed, which used to estimate the distance error mainly based on the error of average per hop distance, unknown nodes and anchor nodes in traditional algorithm. Through the simulation analysis, it can be found that compared with the traditional Location algorithm of DV-Hop and other improved algorithm, the improved algorithm proposed in this paper can reduce the location error and improve the location accuracy when the proportion of the anchor nodes is moderate and the communication radius is small, and this algorithm is suitable for the low power and wide range wireless sensor network environment.

A global and regional prediction and evaluation model of plastic pollution is proposed. We have collected the data of global plastic production and plastic related indicators of hundreds of countries in the past 100 years, and established a global disposable plastic waste accumulation prediction model based on time series model and multiple linear regression. Our model suggests that the earth’s disposable plastic waste will accumulate to the maximum level in 2054, but if we do not intervene in the current development trend of plastic waste, the whole ocean will be seriously polluted in 2024. We divide 145 countries into four categories by clustering algorithm, and establish multiple linear regression models with different leading factors, so as to put forward regional countermeasures. We use factor analysis method to evaluate the plastic pollution in 136 countries, so that the countries can control the plastic pollution more pertinently.

Generalized Maclaurin symmetry mean (GMSM) operator is an effective tool in the process of multi-attribute group decision making (MAGDM) with the characteristic of capturing the interrelationships between multiple arguments. at the same time, q-rung orthopair fuzzy set (q-ROFS) is a good tool to describe uncertainty and fuzziness. To effectively aggregate q-rung orthopair fuzzy information based on the extension of Dombi operations, some novel operators and ideal properties of the proposed operators are put forward in this study. Further, this research present a novel method to MAGDM based on the proposed operators. Finally, the applicability of new method can be proved by a numerical experiment. A detailed parametric analysis and a comparative analysis are also discussed to highlight the flexibility and superiority of the method proposed in this paper.

Sedimentation has become a world-wide issue for almost all types of dam reservoirs. The impact of sedimentation on navigation is mainly reflected in the obstruction of navigation channels, usually at reservoir ends. The issue can be partially solved by joint water-sediment regulation during the drawdown period, called “sedimentation-reduction (SR) regulation”. It aims to enhance scour at shoals by increasing the outflow of the reservoir and accelerates the water level decline. In general, the more inflow discharge or lower dam water level, the better scouring effects, although the waste of water for hydropower generation also needs to be considered. In this work, we propose a model to evaluate the effectiveness of SR regulation schemes in improving navigation condition of reservoirs channels. The constraints for successful regulation operation are analysed in details. The navigation benefits are estimated by the increment of sediment transport volume, and the power generation impacts are estimated by the loss of effective water head. The model gives optimal starting dam water level and corresponding inflow discharge. The results were compared with actual regulation parameters of the Three Gorge reservoir. This work can provide guidelines for reservoir sediment management.

This paper analyzes the scene of UAV executing path monitoring task in dangerous environment, and puts forward a method for UAV path selection in dangerous environment. In the proposed method, the operator mode and the agent operator mode are provided, and the model checking tool prism is used to model and verify the path selection scene of UAV. In normal environment, the UAV performs the path selection under the operator mode, and counts the number of successful and failed photographing of UAV. When the UAV reaches the harsh environment area and loses contact with the operator, the UAV path in the past is analyzed through the agent operator mode, the judgment of the operator is simulated to enable the UAV to continue to carry out the path monitoring task. The UAV road condition monitoring scene in dangerous environment is analyzed and verified from different scales and different operator characteristics.

The groove effect on the tip leakage vortex around a NACA0009 hydrofoil was studied by numerical method. The results show that, as the groove angle is 45°, the groove could enhance the turbulence kinetic energy around the TLV, weaken the TLV strength and improve the pressure in the TLV core, resulting in an efficient suppression effect on the TLV cavitating flow. When the groove angle is 0° and 45°, the groove could decrease the tip leakage flow rate and the TLV gets lower and moves away from the tip gap. As the groove angle is 90° and 135°, the tip leakage flow rate gets larger, and the TLV gets higher and closer to the tip gap.

In this paper, the axial position of the grooves effect on the inlet flow characteristics and hydraulic performance of an axial-flow pump is studied by numerical method. The computational results show that setting the grooves on the pump inlet pipe could significantly improve the inflow characteristics of impeller and the saddle zone would be effectively suppressed. Under the deep stall condition, the grooves could improve the axial velocity of inflow and effectively reducing its streamline bending degree, and it also can block and destroy the vortex structures and then dissipate them rapidly. Reducing the axial distance between grooves and impeller can improve the hydraulic performance and the inflow characteristics of axial-flow pump.

Tunable terahertz optical dual-functional slow light reflector based on gold-graphene metamaterials has been designed and the proposed structure can achieve a good optical reflection effect and slow light effect. The tunable function can be dynamically controlled by applying a voltage to the gold and it can achieve a good effect for selecting reflection band. In more detail, the gold in this device can enable us to dynamically tune the Fermi level of graphene, thus this device can achieve a good tunable effect. Compared with other structures, the graphene monolayer in this structure is simpler and forms a complete band distribution, laying a solid foundation for the subsequent implementation of the device. Through the derivation and analysis of the optical equivalent-cavity coupled mode theory, the theoretical fitting transmission and reflection of this device can be obtained, and they are in good agreement with the numerical results. Furthermore, the slow light effect of this device has been analyzed and it is found that this device has a better slow light performance. This investigation is expected to provide a theoretical basis for the realization of tunable slow light reflectors.

As the urbanization in China advances, urban underground spaces are vigorously developed through large-scale projects. However, the development of underground spaces often proceeds without prior scientific planning and design. City planners who fail to consider geo-environmental factors tend to design nonholistic blueprints that may cause serious problems. The development of an underground space is irreversible because underground resources are currently deemed highly valuable. Therefore, scientific planning must be conducted before the construction and launch of underground space projects. This study adopts a new concept of underground space planning and design and analyzes unfavorable geo-environmental factors, such as sand liquefaction and soft soil, which affect the development of underground spaces. The development mode of the underground space suitable for the geological environment in the study area is proposed. Based on the new concept of planning and design, this work considers a case study in Guangdong Province as a reference for the development mode, depth, and function of underground space in the study area. The findings of this study provide some new planning and design ideas for the development and utilization of underground spaces.

A dual-frequency on–off modulator with considerable modulation depth (MD) and relatively low insertion loss (IL) is performed with patterned monolayer graphene metamaterial. Destructive interference in this structure gives rise to the dual plasmon-induced transparency (DPIT) phenomenon. The coupled mode theory, confirmed by simulated values, is comprehensively introduced to expound the physical mechanism of the DPIT effect. In addition, the influences of the Fermi level on the DPIT transmission spectrum and the carrier mobility of graphene on the on–off modulation are researched. It is found that the dual-frequency on–off modulator exhibits remarkable modulation performance on both switches and is easier to fabricate in operation than other multi-layer graphene-based modulators. In the ‘on1/off1’ state, the MD and IL are 93%, 0.32 dB, respectively. In the ‘on2/off2’ state, the MD and IL are 85%, 0.25 dB, separately. Moreover, the property of slow light reflected by the group index is analyzed. It exhibits that the group index of the proposed structure with multi-channel can reach 358. Thus, the proposed structure stretches the versatile applications in multi-function modulators and multi-channel slow light devices at the terahertz band.

A highly transparent and super-hydrophilic double-layer anti-reflection coating was prepared by a simple and economical sol-gel method based on the glass substrate. The bottom and top layer were respectively made with SiO2–TiO2 sol and SiO2–TiO2-PEG300 sol. The average transmittance of the substrate coated with the double-layer coating is increased by 7% up to 97.4% in the visible light band, and the peak transmittance reaches 99% at about 550 nm. In addition, double-layer anti-reflection coating shows a good super-hydrophilic self-cleaning performance, and the transmittance of the coating can be restored by simple washing with water. Furthermore, due to the existence of anatase TiO2, the composite coating can restore the super-hydrophilicity damaged by dust under UV light.

The recurrent neural network adds the concept of time series on the basis of the traditional multi-layer feedforward neural networks, provides the memory function, and makes the network show good modeling ability on time-series data. Therefore, this paper proposes a LSTM (Long Short-Term Memory) neural network based on memristor. It establishes a discrete weighted LSTM network model by simplifying the traditional recurrent neural network, and uses memristor arrays on the premise of ensuring recognition performance. We realize the function of weight matrix to improve the structure of LSTM neural network, and finally carry out simulation research on the proposed neural network. And due to the volatility and yield of memristors, this paper also demonstrates and analyzes the impact of these two characteristics on network performance, and the performance level of the LSTM neural network based on memristor is verified under the existing preparation level. Experiments on the TIMIT speech database show that the proposed neural network in this paper has good accuracy and its speech recognition performance is superior.

While the shared bicycle without piles solves the problem of people’s “last mile” travel, its free mobility also brings the embarrassing situation of “one car is idle, the other is looking for it”. In this paper, data analysis methods are discussed to reveal the different flow patterns of shared bicycles usage and mobility variation with seasons, weather, time periods, working days, etc; predict method is given for the number of shared bicycle rides based on random forest algorithm. Mastering the distribution and riding rules of shared bicycles can help improve the efficiency of the use of bicycles, better meet the riding needs of users, and provide a data-based basis for regulating the regional regulation of shared bicycles.

With the rapid development of deep learning, histopathological image classification models have made remarkable progress. Recent deep learning-based methods have been applied to raw histopathological images to construct end-to-end models, which avoid hand-craft feature engineering. To construct a model that can capture the intrinsic pattern of the histopathological image dataset, we design a model based on deep metric learning which embeds data points into a Euclidean space. The proposed model trains a deep neural network, which embeds an input image into a Euclidean space where dissimilar images are located far away to each other and vice versa. We adopt a BN-Inception network pretrained on ImageNet as the embedding model. Then it is retrained on target datasets with some triplet loss function. A weighted distance-based triplet sampling strategy is designed to generate hard triplets for the training procedure. Evaluations on benchmark datasets indicate that our deep metric learning-based method outperforms recent successful deep learning models.

As a crucial factor affecting water flooding in tight sandstone reservoirs, dynamic capillary pressure (DCP) has significant impact on the production performance during oil-water flow. In this work, a novel numerical simulation method with DCP is developed to study oil displacement in tight sandstone reservoirs. Based on this new model, the impacts from DCP to water/oil displacement (or water flooding effects) are analysed. The results of this work show that the effects brought by dynamic capillary pressure cannot be neglected. The more significant dynamic effects of capillary pressure correspond to the sample with lower permeability. The effect of DCP is probably a major contributor to non-linear flow (non-Darcy flow) in tight sandstone reservoirs during water flooding process. Compared with the conventional flow theory (e.g., static capillary pressure theory), our derived model with DCP can help to reduce the uncertainty in water/oil flow in tight sandstone reservoirs.

Both black phosphorus (BP) and graphene metamaterials support surface plasmon resonance that is very sensitive to the local change of dielectric properties in the system. We design a novel BP ribbons and graphene sheet hybrid structures based on tunable mid-infrared plasmon-induced transparency. The destructive interference of two excited state modes in the structure leads to a pronounced induced transparent window, and the excellent absorption of the two channels is obtained. The transmission characteristics of the hybrid structures are theoretical calculation, and the results are in good agreement with the numerical simulation curve. The dynamic adjustment function can be realized by adjusting the carrier density of BP and the Fermi level of graphene, and the excellent absorption performance not affected within the adjustment range. Moreover, we found that the optical phase is rapid flips at the plasmon resonance position. Therefore, a sensing medium layer is added on top of the structure to implement a high-sensitivity plasmonic biosensor. This work may provide potential applications to plasmonic devices based on two-dimensional metamaterial.

Here we report the tunable ferroelectricity in superlattices of (Sr1-δTiO3)n/(BaTiO3)8 with δ≈5% and the maximally enhanced ferroelectricity is found in the n=6 superlattice. The room-temperature remnant polarization of n=6 is enhanced by 70% and 1120% compared to those of BaTiO3 and Sr1-δTiO3 thin films, respectively. The ferroelectric transition temperature of n=6 is 40 K higher than that of the BaTiO3 film, and is independent of the measurement frequency. The dependence of remnant field 2Pr on n coincides well with the n dependence of γ/α, which is ratio between out-of-plane and in-plane octahedral rotation angles. It thus suggests a strong correlation between the octahedral rotation pattern and the ferroelectric distortion. Our study demonstrates the importance of constructing superlattices to enhance the ferroelectric properties of the defect-engineered materials to be better than the pristine materials.

A terahertz multifunction modulator composed of upper-layer double graphene ribbons and lower-layer a graphene strip, which can generate a Fano resonance produced by hybrid between a broad mode and a narrow mode, is proposed to realize electro-optical switch and filtering function. The electric field distribution, hybrid theory, and quantum level theory are all employed to explain the Fano resonance, whose transmission spectra are fitted by coupled mode theory. In comparison to other graphene-based terahertz modulators, the amplitude modulation degree can reach 99.57%, meaning an excellent electro-optical switch can be realized. Moreover, the extinction ratio of Fano resonance can reach 99.70%, demonstrating an unparalleled electro-optical filter is implemented. Finally, variations in the lateral and longitudinal lengths of the lower-layer a graphene strip enable excellent dual-band, triple-band filters. Thus, this work provides a new way to implement terahertz multi-function modulators.

Time series data visualization integrates data analysis and mining, computer graphics, interaction design and other technologies and methods. This paper first analyzes the characteristics of time series data, including time and data attributes. Secondly, two kinds of visualization methods are summarized: one is the visualization method of time attribute, including spiral chart, calendar view, theme river view and dynamic view; the other is the visualization method of high dimensional time series data, which mainly summarizes four kinds of parallel coordinate methods. And the visual interaction design method is analyzed. Finally, the visualization of time series data is summarized and prospected.

We construct a laminated metamaterial structure of zipper-type monolayer graphene with silica which equipped with a distinctively dual plasmon induced transparency (PIT) phenomenon. The graphene pattern in this structure crafty connects with the electrode so that we can dynamically control the PIT response (via regulating the bias voltage that applied between the electrode and the substrate to control the graphene Fermi energy). In which, the range of voltage-regulate is calculated theoretically that proved to be a feasible measure for PIT response-tune. Then, the tuning discipline for this structure is summarized from the calculated result of coupled mode theory (CMT) deduced theoretical model and the simulation result. It is found that the structure possesses a great tuning performance within the tuning range we studied; also, such the structure ameliorates the monolayer graphene absorbance from 2.3% to about 50% within a broad dynamic frequency tuning range, the absorption peak frequency modulation depth is up to 45.46%. Besides, the group refractive index is discussed for reflecting the system capability of slow light, and the maximum of the coefficient can catch up to 595. Even if the impact of dielectric light- absorbance to slow light is taken into account, the slow light index at the transparent window is still as high as 200.